{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "<a href=\"https://colab.research.google.com/drive/1vkEt3bYEi7pdqGTgyY1EVB1-zei1X4vA?usp=sharing\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>"
      ],
      "metadata": {
        "id": "C4a6VpnJC6FZ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Meta-Prompting: Task-Agnostic Scaffolding"
      ],
      "metadata": {
        "id": "FUb8H6hRW7Vw"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "C-909w_i66m5"
      },
      "outputs": [],
      "source": [
        "!pip install -qU google-generativeai"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import google.generativeai as genai\n",
        "import getpass"
      ],
      "metadata": {
        "id": "8vvHwzUPXHRJ"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Get free-tier Google's Gemini API Key here: https://aistudio.google.com/app/apikey"
      ],
      "metadata": {
        "id": "CPJ48ZNVXJQE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "API_KEY = getpass.getpass(\"Enter your Google API key: \")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P0pBHxbQXKcx",
        "outputId": "7b398e8d-144b-4da9-a5c6-f4aa4495ac83"
      },
      "execution_count": 4,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Enter your Google API key: ··········\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "genai.configure(api_key=API_KEY)"
      ],
      "metadata": {
        "id": "e73qqkDZXOZ4"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class MetaPromptingAgent:\n",
        "    def __init__(self):\n",
        "        self.model = genai.GenerativeModel(\"gemini-2.0-flash-exp\")\n",
        "        self.expert_library = self._default_experts()\n",
        "\n",
        "    def _default_experts(self):\n",
        "        \"\"\"Library of virtual expert roles\"\"\"\n",
        "        return {\n",
        "            \"Analyst\": \"Analyzes data, trends, and patterns objectively\",\n",
        "            \"Engineer\": \"Provides technical and implementation perspectives\",\n",
        "            \"Doctor\": \"Offers medical and health-related expertise\",\n",
        "            \"Lawyer\": \"Understands legal, compliance, and regulatory issues\",\n",
        "            \"Economist\": \"Analyzes financial, economic, and market aspects\",\n",
        "            \"Designer\": \"Focuses on user experience, aesthetics, and usability\",\n",
        "            \"Scientist\": \"Applies scientific methodology and research principles\",\n",
        "            \"Psychologist\": \"Understands human behavior and mental processes\",\n",
        "            \"Strategist\": \"Develops plans, strategies, and long-term thinking\",\n",
        "            \"Ethicist\": \"Evaluates moral and ethical implications\"\n",
        "        }\n",
        "\n",
        "    def decompose_task(self, query):\n",
        "        \"\"\"Break down complex problem into sub-tasks\"\"\"\n",
        "        experts_list = \"\\n\".join([f\"- {name}: {desc}\" for name, desc in self.expert_library.items()])\n",
        "\n",
        "        prompt = f\"\"\"You are a Meta-Orchestrator. Break down this complex query into 2-4 sub-tasks.\n",
        "\n",
        "        Query: {query}\n",
        "\n",
        "        Available Experts:\n",
        "        {experts_list}\n",
        "\n",
        "        For each sub-task, specify:\n",
        "        1. The sub-task description\n",
        "        2. Which expert should handle it\n",
        "\n",
        "        Format:\n",
        "        Sub-task N: [description]\n",
        "        Expert: [expert_name]\n",
        "\n",
        "        Decomposition:\"\"\"\n",
        "\n",
        "        response = self.model.generate_content(prompt).text\n",
        "        return self._parse_decomposition(response)\n",
        "\n",
        "    def _parse_decomposition(self, text):\n",
        "        \"\"\"Parse decomposition into structured format\"\"\"\n",
        "        subtasks = []\n",
        "        current_task = {}\n",
        "\n",
        "        for line in text.split(\"\\n\"):\n",
        "            line = line.strip()\n",
        "\n",
        "            if line.startswith(\"Sub-task\") or line.startswith(\"Subtask\"):\n",
        "                if current_task:\n",
        "                    subtasks.append(current_task)\n",
        "                # Extract description after colon\n",
        "                if \":\" in line:\n",
        "                    current_task = {\"description\": line.split(\":\", 1)[1].strip()}\n",
        "                else:\n",
        "                    current_task = {\"description\": line}\n",
        "\n",
        "            elif line.startswith(\"Expert:\"):\n",
        "                expert = line.split(\":\", 1)[1].strip()\n",
        "                # Match expert name from library\n",
        "                for expert_name in self.expert_library.keys():\n",
        "                    if expert_name.lower() in expert.lower():\n",
        "                        current_task[\"expert\"] = expert_name\n",
        "                        break\n",
        "\n",
        "        if current_task:\n",
        "            subtasks.append(current_task)\n",
        "\n",
        "        return subtasks\n",
        "\n",
        "    def simulate_expert(self, expert_name, subtask, original_query):\n",
        "        \"\"\"Simulate a virtual expert responding to sub-task\"\"\"\n",
        "        expert_desc = self.expert_library.get(expert_name, \"a general expert\")\n",
        "\n",
        "        prompt = f\"\"\"You are a virtual {expert_name}. {expert_desc}\n",
        "\n",
        "        Original Query: {original_query}\n",
        "\n",
        "        Your Specific Sub-task: {subtask}\n",
        "\n",
        "        Provide your expert analysis and recommendations:\"\"\"\n",
        "\n",
        "        response = self.model.generate_content(prompt).text\n",
        "        return response.strip()\n",
        "\n",
        "    def synthesize_responses(self, query, expert_responses):\n",
        "        \"\"\"Combine expert outputs into coherent answer\"\"\"\n",
        "        responses_text = \"\\n\\n\".join([\n",
        "            f\"{expert_name} ({expert_desc}):\\n{response}\"\n",
        "            for expert_name, expert_desc, response in expert_responses\n",
        "        ])\n",
        "\n",
        "        prompt = f\"\"\"Original Query: {query}\n",
        "\n",
        "        Expert Responses:\n",
        "        {responses_text}\n",
        "\n",
        "        Synthesize these expert perspectives into a comprehensive, coherent answer:\"\"\"\n",
        "\n",
        "        response = self.model.generate_content(prompt).text\n",
        "        return response.strip()\n",
        "\n",
        "    def solve(self, query):\n",
        "        \"\"\"Main meta-prompting pipeline\"\"\"\n",
        "        print(f\"\\n{'='*60}\")\n",
        "        print(f\"🎭 Meta-Prompting (Multi-Expert Simulation)\")\n",
        "        print(f\"{'='*60}\")\n",
        "        print(f\"Query: {query}\\n\")\n",
        "\n",
        "        # Step 1: Decompose task\n",
        "        print(f\"{'─'*60}\")\n",
        "        print(f\"STEP 1: Task Decomposition\")\n",
        "        print(f\"{'─'*60}\\n\")\n",
        "\n",
        "        subtasks = self.decompose_task(query)\n",
        "\n",
        "        print(f\"Identified {len(subtasks)} sub-tasks:\\n\")\n",
        "        for i, task in enumerate(subtasks, 1):\n",
        "            expert = task.get(\"expert\", \"Unknown\")\n",
        "            desc = task.get(\"description\", \"N/A\")\n",
        "            print(f\"{i}. [{expert}] {desc}\")\n",
        "        print()\n",
        "\n",
        "        # Step 2: Route to virtual experts\n",
        "        print(f\"{'─'*60}\")\n",
        "        print(f\"STEP 2: Consulting Virtual Experts\")\n",
        "        print(f\"{'─'*60}\\n\")\n",
        "\n",
        "        expert_responses = []\n",
        "\n",
        "        for i, task in enumerate(subtasks, 1):\n",
        "            expert_name = task.get(\"expert\")\n",
        "            if not expert_name or expert_name not in self.expert_library:\n",
        "                print(f\"⚠️  Skipping task {i}: No valid expert assigned\\n\")\n",
        "                continue\n",
        "\n",
        "            subtask_desc = task.get(\"description\", \"\")\n",
        "            expert_desc = self.expert_library[expert_name]\n",
        "\n",
        "            print(f\"👤 Consulting {expert_name}...\")\n",
        "            print(f\"   Task: {subtask_desc[:60]}...\")\n",
        "\n",
        "            response = self.simulate_expert(expert_name, subtask_desc, query)\n",
        "\n",
        "            print(f\"   Response: {response[:100]}...\\n\")\n",
        "\n",
        "            expert_responses.append((expert_name, expert_desc, response))\n",
        "\n",
        "        # Step 3: Synthesize\n",
        "        print(f\"{'─'*60}\")\n",
        "        print(f\"STEP 3: Synthesizing Expert Responses\")\n",
        "        print(f\"{'─'*60}\\n\")\n",
        "\n",
        "        final_answer = self.synthesize_responses(query, expert_responses)\n",
        "\n",
        "        print(f\"{'='*60}\")\n",
        "        print(f\"💡 FINAL ANSWER\")\n",
        "        print(f\"{'='*60}\")\n",
        "        print(final_answer)\n",
        "        print()\n",
        "\n",
        "        return final_answer"
      ],
      "metadata": {
        "id": "RDGCVEpqXTvC"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Example 1: Multi-Domain Question\n",
        "print(\"=\"*60)\n",
        "print(\"EXAMPLE 1: Multi-Domain Question\")\n",
        "print(\"=\"*60)\n",
        "\n",
        "agent1 = MetaPromptingAgent()\n",
        "agent1.solve(\n",
        "    \"Should our company launch a new AI-powered health app? \"\n",
        "    \"Consider technical feasibility, legal compliance, market opportunity, and ethical implications.\"\n",
        ")\n",
        "\n",
        "\n",
        "# Example 2: Complex Business Decision\n",
        "print(\"\\n\" + \"=\"*60)\n",
        "print(\"EXAMPLE 2: Complex Business Decision\")\n",
        "print(\"=\"*60)\n",
        "\n",
        "agent2 = MetaPromptingAgent()\n",
        "agent2.solve(\n",
        "    \"We're considering a 4-day work week. Analyze the impact on productivity, \"\n",
        "    \"employee wellbeing, financial costs, and legal requirements.\"\n",
        ")\n",
        "\n",
        "\n",
        "# Example 3: Product Development\n",
        "print(\"\\n\" + \"=\"*60)\n",
        "print(\"EXAMPLE 3: Product Development Analysis\")\n",
        "print(\"=\"*60)\n",
        "\n",
        "agent3 = MetaPromptingAgent()\n",
        "agent3.solve(\n",
        "    \"Design a smart home security system. Consider engineering requirements, \"\n",
        "    \"user experience design, privacy concerns, and market positioning.\"\n",
        ")\n",
        "\n",
        "\n",
        "# Example 4: Policy Evaluation\n",
        "print(\"\\n\" + \"=\"*60)\n",
        "print(\"EXAMPLE 4: Policy Evaluation\")\n",
        "print(\"=\"*60)\n",
        "\n",
        "agent4 = MetaPromptingAgent()\n",
        "agent4.solve(\n",
        "    \"Evaluate a proposed carbon tax policy. Analyze economic impact, \"\n",
        "    \"environmental benefits, legal framework, and public perception.\"\n",
        ")\n",
        "\n",
        "\n",
        "# Example 5: Healthcare Decision\n",
        "print(\"\\n\" + \"=\"*60)\n",
        "print(\"EXAMPLE 5: Healthcare Strategy\")\n",
        "print(\"=\"*60)\n",
        "\n",
        "agent5 = MetaPromptingAgent()\n",
        "agent5.solve(\n",
        "    \"A hospital wants to implement AI diagnostics. Assess medical effectiveness, \"\n",
        "    \"ethical considerations, legal liability, and implementation costs.\"\n",
        ")\n",
        "\n",
        "\n",
        "# Example 6: Creative Problem Solving\n",
        "print(\"\\n\" + \"=\"*60)\n",
        "print(\"EXAMPLE 6: Creative Solution Development\")\n",
        "print(\"=\"*60)\n",
        "\n",
        "agent6 = MetaPromptingAgent()\n",
        "agent6.solve(\n",
        "    \"How can we reduce urban traffic congestion? \"\n",
        "    \"Consider engineering solutions, economic incentives, policy changes, and behavioral psychology.\"\n",
        ")\n",
        "\n",
        "\n",
        "# Example 7: Technology Assessment\n",
        "print(\"\\n\" + \"=\"*60)\n",
        "print(\"EXAMPLE 7: Technology Assessment\")\n",
        "print(\"=\"*60)\n",
        "\n",
        "agent7 = MetaPromptingAgent()\n",
        "agent7.solve(\n",
        "    \"Should we adopt blockchain for our supply chain? \"\n",
        "    \"Evaluate technical capabilities, security implications, cost-benefit analysis, and strategic fit.\"\n",
        ")\n",
        "\n",
        "\n",
        "# Example 8: Custom Experts\n",
        "print(\"\\n\" + \"=\"*60)\n",
        "print(\"EXAMPLE 8: Custom Expert Configuration\")\n",
        "print(\"=\"*60)\n",
        "\n",
        "# Create agent with custom experts\n",
        "custom_agent = MetaPromptingAgent()\n",
        "custom_agent.expert_library = {\n",
        "    \"Marketing Expert\": \"Specializes in brand positioning and customer acquisition\",\n",
        "    \"Data Scientist\": \"Analyzes data patterns and builds predictive models\",\n",
        "    \"Operations Manager\": \"Optimizes processes and resource allocation\",\n",
        "    \"Customer Success\": \"Understands user needs and satisfaction\"\n",
        "}\n",
        "\n",
        "custom_agent.solve(\n",
        "    \"How can we improve customer retention for our SaaS product?\"\n",
        ")\n",
        "\n",
        "\n",
        "print(\"✅ Meta-Prompting Complete!\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "WlZSLun7XiVt",
        "outputId": "a62a117a-aab9-4854-f3e1-fa2d889d16df"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "============================================================\n",
            "EXAMPLE 1: Multi-Domain Question\n",
            "============================================================\n",
            "\n",
            "============================================================\n",
            "🎭 Meta-Prompting (Multi-Expert Simulation)\n",
            "============================================================\n",
            "Query: Should our company launch a new AI-powered health app? Consider technical feasibility, legal compliance, market opportunity, and ethical implications.\n",
            "\n",
            "────────────────────────────────────────────────────────────\n",
            "STEP 1: Task Decomposition\n",
            "────────────────────────────────────────────────────────────\n",
            "\n",
            "Identified 4 sub-tasks:\n",
            "\n",
            "1. [Economist] **Market Assessment and Financial Viability:** Analyze the market opportunity for an AI-powered health app, including market size, competitive landscape, potential revenue streams, and financial projections. Assess the economic viability of developing and launching the app.\n",
            "2. [Engineer] **Technical Feasibility and Development Roadmap:** Evaluate the technical feasibility of building the proposed AI-powered health app. Define the necessary technology stack, infrastructure requirements, development timeline, and potential challenges. Consider data security, privacy, and integration with existing systems.\n",
            "3. [Unknown] **Legal and Ethical Compliance Review:** Conduct a comprehensive review of the legal and ethical implications of the AI-powered health app. This includes compliance with data privacy regulations (e.g., HIPAA, GDPR), liability concerns, and potential biases in the AI algorithms. Evaluate the ethical considerations related to data usage, transparency, and potential impact on healthcare access and equity.\n",
            "4. [Unknown] **Clinical Benefit and User Experience:** Evaluate the clinical benefit the app can provide and if it is better than existing solutions. Asses the usability, appeal, and user experience of the proposed app, ensuring it meets the needs of the target users and is designed in a way that promotes engagement and positive health outcomes. Consult with the Doctor to confirm the app provides a health benefit and does not lead to any harm or incorrect medical recommendations.\n",
            "\n",
            "────────────────────────────────────────────────────────────\n",
            "STEP 2: Consulting Virtual Experts\n",
            "────────────────────────────────────────────────────────────\n",
            "\n",
            "👤 Consulting Economist...\n",
            "   Task: **Market Assessment and Financial Viability:** Analyze the m...\n",
            "   Response: Okay, let's delve into the market assessment and financial viability of launching a new AI-powered h...\n",
            "\n",
            "👤 Consulting Engineer...\n",
            "   Task: **Technical Feasibility and Development Roadmap:** Evaluate ...\n",
            "   Response: Okay, let's dive into the technical feasibility and development roadmap for your proposed AI-powered...\n",
            "\n",
            "⚠️  Skipping task 3: No valid expert assigned\n",
            "\n",
            "⚠️  Skipping task 4: No valid expert assigned\n",
            "\n",
            "────────────────────────────────────────────────────────────\n",
            "STEP 3: Synthesizing Expert Responses\n",
            "────────────────────────────────────────────────────────────\n",
            "\n",
            "============================================================\n",
            "💡 FINAL ANSWER\n",
            "============================================================\n",
            "## Launching an AI-Powered Health App: A Comprehensive Analysis\n",
            "\n",
            "Launching an AI-powered health app presents a significant opportunity, but success hinges on careful consideration of market viability, technical feasibility, legal compliance, and ethical implications.  A balanced approach, incorporating insights from both economic and engineering perspectives, is crucial for making an informed decision.\n",
            "\n",
            "**I. Market Opportunity and Economic Viability (Economist's Perspective):**\n",
            "\n",
            "*   **Significant Market Potential:** The digital health market, particularly the AI-powered segment, is experiencing rapid growth, driven by aging populations, rising healthcare costs, increased tech adoption, and AI advancements.  Key sub-segments include diagnostics, personalized medicine, remote monitoring, and mental health support. Expect double-digit growth rates in the coming years.\n",
            "*   **Target Market Definition is Key:**  Precisely define your target audience based on demographics, health conditions, tech savviness, and insurance coverage.  Identify unmet needs your app can address more effectively than existing solutions, focusing on accuracy, personalization, user experience, and data privacy.\n",
            "*   **Competitive Advantage is Essential:**  Analyze direct competitors (AI-powered health apps) and indirect competitors (traditional healthcare providers, telehealth platforms without AI).  Define your app's unique value proposition – superior AI algorithms, intuitive design, personalized features, robust data security, and seamless integration with other platforms are crucial.\n",
            "*   **Diverse Revenue Streams:** Explore various revenue models, including subscription (tiered pricing), freemium, in-app purchases, partnerships with healthcare providers, and potentially data licensing (anonymized and aggregated, with strict adherence to privacy regulations and user consent).\n",
            "*   **Detailed Financial Projections are a Must:**  Develop comprehensive financial projections, including development costs (software, AI algorithm, UI/UX, data acquisition, regulatory compliance), marketing costs (ASO, digital marketing, PR), and operating costs (server, customer support, data maintenance, security, updates).  Project revenue over 3-5 years, calculate profitability, ROI, break-even point, and conduct sensitivity analysis.\n",
            "*   **Economic Viability Assessment:**  Calculate NPV (Net Present Value), IRR (Internal Rate of Return), and Payback Period to assess financial attractiveness.  A positive NPV and an IRR exceeding the company's hurdle rate are essential.\n",
            "*   **Recommendations:**\n",
            "    *   **Thorough Market Research:** Validate assumptions about market size, growth, and competition through focus groups, surveys, and competitor analysis.\n",
            "    *   **MVP Approach:** Develop a Minimum Viable Product (MVP) to test the market and gather user feedback before significant investment.\n",
            "    *   **Niche Focus:** Target a specific niche within the AI-powered health market to concentrate marketing efforts and build a strong competitive advantage.\n",
            "    *   **Prioritize Data Privacy:**  Ensure compliance with all relevant data privacy regulations (HIPAA, GDPR, etc.) and implement robust security measures. Transparency with users is key.\n",
            "    *   **Secure Funding:** Explore funding options like venture capital, angel investors, or government grants.\n",
            "    *   **Build a Strong Team:** Assemble a team with expertise in AI, healthcare, software development, and marketing.\n",
            "    *   **Continuous Monitoring and Adaptation:**  Adapt to the rapidly evolving market and changing user needs.\n",
            "\n",
            "**II. Technical Feasibility and Development Roadmap (Engineer's Perspective):**\n",
            "\n",
            "*   **AI Model Feasibility:** Dependent on the specific use cases.  Data availability, quality, and labeling are critical.  Start with simpler models and increase complexity only if needed.  Cloud-based resources are generally the most cost-effective for training.\n",
            "*   **Mobile App Development:** Highly feasible with existing frameworks and tools.  Prioritize UI/UX design, performance optimization (battery life, data usage), and offline functionality.\n",
            "*   **Backend Infrastructure:** Cloud-based solutions are highly feasible and offer scalability.  Key components include a suitable database (encrypted and HIPAA compliant), well-defined APIs, serverless functions, and cloud storage.\n",
            "*   **Data Security and Privacy:** Achievable with robust measures: end-to-end encryption, strict access control (RBAC), secure authentication (multi-factor), regular security audits, data anonymization/pseudonymization, and HIPAA compliance built from the ground up.\n",
            "*   **Integration with Existing Systems:**  Feasibility depends on the existing systems.  Use interoperability standards like FHIR and ensure API compatibility.  Address security concerns during integration.\n",
            "*   **Technology Stack Recommendation:** React Native (or native iOS/Android development), cloud provider (AWS, Azure, GCP), PostgreSQL/MongoDB, RESTful APIs (Node.js, Python, Java), serverless functions, and AI/ML framework (TensorFlow, PyTorch, scikit-learn).\n",
            "*   **Development Timeline (Estimate):**\n",
            "    *   **Phase 1 (3-6 months):** Proof of Concept - Market research, AI model development, basic backend, prototype app, initial security assessment.\n",
            "    *   **Phase 2 (6-9 months):** MVP - Refine AI models, develop core features, implement robust security, user testing, HIPAA compliance preparation.\n",
            "    *   **Phase 3 (3-6 months):** Full Product Launch - Finalize AI models, develop additional features, thorough security audits, marketing implementation.\n",
            "    *   **Phase 4 (Continuous):** Ongoing Maintenance and Improvements - Monitor performance, update AI models, address security vulnerabilities, gather user feedback.\n",
            "*   **Potential Challenges and Mitigation Strategies:**\n",
            "    *   **Data Acquisition and Quality:** Collaborate with healthcare providers, use public datasets ethically, consider synthetic data cautiously, prioritize data cleaning.\n",
            "    *   **AI Model Accuracy and Bias:** Rigorous evaluation, bias detection and mitigation, explainable AI (XAI), continuous monitoring and retraining.\n",
            "    *   **Data Security and Privacy Compliance:** End-to-end encryption, strong access control, regular security audits, data anonymization, legal and security experts.\n",
            "    *   **Integration with Existing Systems:** Use FHIR, develop well-defined APIs, thorough testing, plan integration early.\n",
            "    *   **Scalability and Performance:** Cloud-based infrastructure, code optimization, caching mechanisms, load testing.\n",
            "    *   **Regulatory Approval (If Required):** Consult regulatory experts early, comply with regulations, prepare for a lengthy approval process.\n",
            "*   **Recommendations:**\n",
            "    *   **Start Small and Iterate:** Focus on a narrow set of features for the initial MVP.\n",
            "    *   **Prioritize Security and Privacy:** Make security and privacy a top priority throughout development.\n",
            "    *   **Engage with Experts:** Consult with AI/ML engineers, mobile app developers, cloud architects, security experts, and legal counsel with healthcare experience.\n",
            "    *   **Plan for Continuous Improvement:** Establish a process for continuous improvement and model refinement.\n",
            "    *   **User-Centric Design:** Focus on creating a user-friendly and intuitive app experience.\n",
            "    *   **Thorough Documentation:** Document everything for maintainability and compliance.\n",
            "\n",
            "**III. Legal and Ethical Considerations (Implicit in both perspectives):**\n",
            "\n",
            "*   **HIPAA and GDPR Compliance:**  Crucial for protecting patient data privacy.\n",
            "*   **Data Security:** Implement robust security measures to prevent data breaches.\n",
            "*   **Transparency:** Be transparent with users about how their data is being used.  Obtain informed consent.\n",
            "*   **Bias in AI Algorithms:**  Address potential bias in AI algorithms to ensure fairness and avoid discrimination.\n",
            "*   **Explainability:** Strive for explainable AI (XAI) to understand the reasoning behind AI decisions, especially in diagnostic or treatment recommendations.\n",
            "*   **Regulatory Approval (FDA, etc.):** Determine if regulatory approval is required based on the app's functionality and claims.\n",
            "*   **Liability:**  Clearly define the app's limitations and disclaimers to mitigate potential liability.\n",
            "\n",
            "**IV. Conclusion:**\n",
            "\n",
            "Launching an AI-powered health app presents a substantial market opportunity, but success requires a comprehensive approach that balances economic viability with technical feasibility, legal compliance, and ethical considerations.  Thorough planning, a strong team, and a commitment to continuous improvement are essential for navigating the complexities of this rapidly evolving field. A phased approach, starting with an MVP and prioritizing security and privacy, will increase the chances of success. A \"go/no-go\" decision should be based on a positive economic viability assessment, a realistic technical roadmap, and a clear understanding of the legal and ethical implications.\n",
            "\n",
            "\n",
            "============================================================\n",
            "EXAMPLE 2: Complex Business Decision\n",
            "============================================================\n",
            "\n",
            "============================================================\n",
            "🎭 Meta-Prompting (Multi-Expert Simulation)\n",
            "============================================================\n",
            "Query: We're considering a 4-day work week. Analyze the impact on productivity, employee wellbeing, financial costs, and legal requirements.\n",
            "\n",
            "────────────────────────────────────────────────────────────\n",
            "STEP 1: Task Decomposition\n",
            "────────────────────────────────────────────────────────────\n",
            "\n",
            "Identified 4 sub-tasks:\n",
            "\n",
            "1. [Psychologist] Analyze the impact of a 4-day work week on employee productivity and wellbeing, considering factors like job satisfaction, stress levels, and overall mental health.\n",
            "2. [Economist] Analyze the potential financial implications of a 4-day work week, including changes in operational costs, potential revenue impact, and any necessary adjustments to compensation or benefits.\n",
            "3. [Lawyer] Investigate the legal and regulatory implications of implementing a 4-day work week, including labor laws, overtime regulations, and compliance requirements.\n",
            "4. [Strategist] Synthesize the findings from the previous sub-tasks, creating a comprehensive report detailing the potential benefits, risks, and challenges of transitioning to a 4-day work week, along with strategic recommendations for implementation.\n",
            "\n",
            "────────────────────────────────────────────────────────────\n",
            "STEP 2: Consulting Virtual Experts\n",
            "────────────────────────────────────────────────────────────\n",
            "\n",
            "👤 Consulting Psychologist...\n",
            "   Task: Analyze the impact of a 4-day work week on employee producti...\n",
            "   Response: Okay, let's delve into the impact of a 4-day work week on employee productivity and wellbeing. As a ...\n",
            "\n",
            "👤 Consulting Economist...\n",
            "   Task: Analyze the potential financial implications of a 4-day work...\n"
          ]
        }
      ]
    },
    {
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      "source": [],
      "metadata": {
        "id": "3BC-fYG9XmbA"
      },
      "execution_count": null,
      "outputs": []
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}